Point-of-Care Noninvasive Prediction of Liver-Related Events in Patients With Nonalcoholic Fatty Liver Disease

Background & Aims: Individual risk prediction of liver-related events (LRE) is needed for clinical assessment of nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH) patients. We aimed to provide point-of-care validated liver stiffness measurement (LSM)-based risk prediction models for the development of LRE in patients with NAFLD, focusing on selecting patients for clinical trials at risk of clinical events. Methods: Two large multicenter cohorts were evaluated, 2638 NAFLD patients covering all LSM values as the derivation cohort and 679 more advanced patients as the validation cohort. We used Cox regression to develop and validate risk prediction models based on LSM alone, and the ANTICIPATE and ANTICIPATE-NASH models for clinically significant portal hypertension. The main outcome of the study was the rate of LRE in the first 3 years after initial assessment. Results: The 3 predictive models had similar performance in the derivation cohort with a very high discriminative value (c-statistic, 0.87–0.91). In the validation cohort, the LSM-LRE alone model had a significant inferior discrimination (c-statistic, 0.75) compared with the other 2 models, whereas the ANTICIPATE-NASH-LRE model (0.81) was significantly better than the ANTICIPATE-LRE model (0.79). In addition, the ANTICIPATE-NASH-LRE model presented very good calibration in the validation cohort (integrated calibration index, 0.016), and was better than the ANTICIPATE-LRE model. Conclusions: The ANTICIPATE-LRE models, and especially the ANTICIPATE-NASH-LRE model, could be valuable validated clinical tools to individually assess the risk of LRE at 3 years in patients with NAFLD/NASH.